Method for biomarker and drug-target discovery for prostate cancer diagnosis and treatment as well as biomarker assays determined therewith

ABSTRACT

The invention relates to a method for the determination of a cancer diagnostic/therapeutic biomarker assay and drug-targets including the following steps: (a) identification of potential candidate protein/peptide biomarkers and drug-targets based on the measurement of protein/peptide constituent concentrations in tissue sample proteomes as well as serum, plasma or any other derivatives of blood, or blood itself sample proteomes derived from healthy non-human mammalian individuals as well as from cancerous non-human mammalian individuals and qualitatively selecting as potential candidate protein/peptide biomarkers those which show a pronounced differential behaviour between healthy and cancerous sample proteomes; (b) optional verification of the potential candidate protein/peptide biomarkers as identified in step (a) by quantitative mass spectrometric measurement of the potential candidate protein biomarkers in serum, plasma or any other derivatives of blood, or blood itself sample proteomes derived from healthy non-human mammalian individuals as well as from cancerous non-human mammalian individuals and selecting as candidate protein/peptide biomarkers those which show a mass-spectrometrically measurable quantitative differential behaviour between healthy and cancerous sample proteomes; (c) validation of the candidate protein/peptide biomarkers as identified in step (a), or as optionally verified in step (b), by mass spectrometric measurement and/or antibody-based assays such as an Enzyme-Linked Immunosorbent Assay (ELISA) determination of the candidate protein biomarkers in serum, plasma or any other derivatives of blood, or blood itself sample proteomes derived from healthy human individuals as well as from cancerous human individuals and selecting as protein/peptide biomarkers those which show a mass-spectrometrically measurable and/or antibody-based assay detectable differential behaviour between healthy and cancerous sample proteomes; (d) application of statistical methods to uncover single or groups of protein/peptide biomarkers as validated in step (c) as signatures for the detection of patients with cancer. The invention furthermore relates to specific biomarker assays for the highly reliable diagnosis of cancer, specifically of localized or non-localized prostate cancer, using human serum, plasma or any other derivatives of blood, or blood itself.

TECHNICAL FIELD

The present invention relates to the field of methods for thedetermination of biomarker assays and/or drug-targets for the diagnosisof cancer and its treatment and/or prognosis, specifically of prostatecancer, be it localized or non-localized prostate cancer. A furtherobject of the present invention is to propose specific biomarker assaysfor these diagnostic purposes and/or patient stratification as well asmethods for diagnosis using these specific biomarker assays.

BACKGROUND OF THE INVENTION

The diagnosis and treatment of prostate cancer, despite decennialresearch efforts, are still a major challenge in the clinics. Prostatecancer progression is unfortunately silent, and an early detection offaster progressing and potentially dangerous lesions is crucial for thepatient's health, since complete remission and cure from the disease ispossible only at early stages of the disease.

The best noninvasive diagnostic test available for prostate cancer isthe detection of the Prostate Specific Antigen (PSA) in the bloodcoupled with digital rectal examination (DRE). PSA is a protein producedby the epithelial cells of the prostate gland. PSA is also known askallikrein III, seminin, semenogelase, γ-seminoprotein and P-30 antigenand it is a 34 kD glycoprotein present in small quantities in the serumof normal men, and is often elevated in the presence of prostate cancerand in other prostate disorders. A blood test to measure PSA coupledwith DRE is the most effective test currently available for the earlydetection of prostate cancer. Higher-than-normal levels of PSA areassociated with both localized (loc) and metastatic (met) prostatecancer (CaP).

The diagnostic accuracy of PSA alone is only around 60% and themethodology has major drawbacks in specificity (too many false positivescases that undergo unneeded prostate biopsy or surgery). Indeed PSAlevels can be also increased by prostate infection, irritation, benignprostatic hypertrophy (enlargement) or hyperplasia (BPH), and recentejaculation, producing a false positive result.

A reliable and non-invasive diagnostic/prognostic procedure is thusstill lacking, even tough novel methodologies based on the simultaneousmeasurement of various parameters (e.g. free and total PSA) are emergingas tools to increase the overall diagnostic accuracy. Most PSA in theblood is bound to serum proteins. A small amount is not protein boundand is called free PSA. In men with prostate cancer the ratio of free(unbound) PSA to total PSA is decreased. The risk of cancer increases ifthe free to total ratio is less than 25%. The lower the ratio, thegreater the probability of prostate cancer. However, both total and freePSA increase immediately after ejaculation, returning slowly to baselinelevels within 24 hours, and also other mechanisms not related to CaP caninfluence the free to total PSA ratio.

Similar to diagnosis, treatment and/or prognosis of prostate cancerremains a major challenge due to heterogeneity of the disease. Althoughmultiple mechanisms of prostate cancer have been suggested, the lack ofsuitable signatures able to stratify patients and key target proteinsfor therapeutic intervention cures are still not within reach.

SUMMARY OF THE INVENTION

The object of the present invention is therefore to provide an improvedmethod for the determination of biomarker assays and/or drug-targets fordiagnosis, prognosis, treatment as well as for monitoring of treatmentof cancer, and/or for the stratification of patients, specifically ofprostate cancer, be it localized or non-localized prostate cancer. Itshould be noted that from a principle point of view the proposed methodis not limited to cancer but can be applied to any kind of human oranimal disease or dysfunction. From a practical point of view the onlylimitation can sometimes be that a model system should be availablewhich can be used for the translational approach as described below.

It should be noted that not only candidates from a mouse (or generallyanimal, e.g. non-human model) are part of the invention. Potentialmarker candidates can be determined from a variety of sources, includingalso human tissue, proximal fluids, animal models cell lines, datamining etc.

There is however a rather distinctive advantage of the animal model, inthe more general context of a systems biology approach to biomarkerdiscovery. Assuming that in different cancers (that affect differenttissues) cellular networks are perturbed. Assuming further thatdifferent manifestations of cancer can have the same or overlappingperturbations, an animal such as a mouse model allows to specificallyapply one perturbation in isolation or defined combinations ofperturbations to determine how the target tissue reacts to thisperturbation. If one then furthermore assumes that some of the proteinsthat constitute this response, (either direct effects of theperturbation, e.g. loss of phosphopeptides if a kinase is deleted ormutated) or compensatory effects leave specific fingerprints in thetarget tissue, some of these fingerprints are detectable in serum usingthe methods we describe here. A distinctive feature of a geneticallydefined mouse model allows to define changes associated with a specificgene mutation (in our case e.g. PTEN) that we know is mutated also inhuman cancer and thus immediately suggests a subclass of patients to belooked at and treated (personalized medicine). In planning clinicaltrials it is often important to have solid knowledge of the prevalenceand frequency of molecular marker species in the diseased population.Those patients with a high likelihood of good response may be selectedin a so called patient stratification process. Based on this informationthe size of the available cohort can be estimated for a given strictmarker profile. Retrospective studies in archived tissues e.g. allowdetermining those parameters fast and early before the design for theclinical phase has to be fixed and committed.

A further object of the present invention is to propose specificbiomarker assays for thesediagnostic/therapeutic/monitoring/prognostic/patient stratificationpurposes as well as methods fordiagnosis/therapy/monitoring/prognosis/patient stratification usingthese specific biomarker assays.

The present invention according to a first aspect thus relates to amethod for the determination of a cancer (or generally speakingdisease/dysfunction)diagnostic/therapeutic/monitoring/prognostic/patient stratificationbiomarker assay including the following steps:

(a) identification of potential candidate protein/peptide biomarkersbased on the measurement of protein/peptide constituent concentrations(abundances) in tissue sample proteomes as well as sample proteomes ofserum, plasma or any other derivatives of blood, or blood itself derivedfrom healthy non-human mammalian individuals as well as from cancerousnon-human mammalian individuals and qualitatively selecting as potentialcandidate protein/peptide biomarkers those which show a pronounceddifferential behaviour between healthy and cancerous sample proteomes.As pointed out above, in this step not necessarily non-human sampleshave to be used, also human sources can be used for this step such ashuman tissue, proximal fluids etc.

This step can optionally be followed by step (b): verification of thepotential candidate protein/peptide biomarkers as identified in step (a)by quantitative mass spectrometric measurement of the potentialcandidate protein biomarkers in sample proteomes of serum, plasma or anyother derivatives of blood, or blood itself derived from healthynon-human mammalian individuals as well as from cancerous non-humanmammalian individuals and selecting as candidate protein/peptidebiomarkers those which show a mass-spectrometrically measurablequantitative differential behaviour between healthy and cancerous sampleproteomes. Of course mass spectroscopy is just one and indeed thepreferred way of measurement in this verification step. Also differentmethods for example using an affinity reagents, can be used in a waysimilar or identical to the one that has finally to be used for thediagnosis/prognosis/therapy.

Then follows a step (c): validation of the candidate protein/peptidebiomarkers as identified in step (a), or as optionally verified in step(b), by mass spectrometric measurement and/or antibody-baseddetermination of the candidate protein biomarkers in sample proteomes ofserum, plasma or any other derivatives of blood, or blood itself derivedfrom healthy human individuals as well as from cancerous humanindividuals and selecting as protein/peptide biomarkers those which showa mass-spectrometrically measurable and/or affinity reagent-based assay,preferably antibody-based assay detectable differential behaviourbetween healthy and cancerous sample proteomes;

(d) application of statistical methods to uncover single or groups ofprotein/peptide biomarkers as validated in step (c) as signatures forthe detection of patients with cancer.

Preferably, the affinity reagent-based determination is, as mentionedabove, an antibody-based determination method/assay, and is for exampleselected to be an Enzyme-Linked Immunosorbent Assay (ELISA) or aMultiplex Bead Array Assay or other methodologies aiming at measuring aparticular protein concentration.

As mentioned above, the method can not only be applied for thedetermination of cancer biomarker systems but also to the determinationof biomarker systems for other kinds of diseases or dysfunctions of anorganism. In these cases in the above methods (and also in thediscussion further below of the specification) the expression“cancerous” (for example for the sample) is essentially to be replacedby an expression “diseased” or “dysfunctional”.

One of the gists of the present invention is therefore the concept toincrease the accuracy of the non-invasive diagnostic procedure for thedetection of (prostate) cancer on the one hand, and to identify newtherapeutical/imaging targets used in the clinical practice. We haveestablished a protocol for (prostate) cancer biomarkers and/ordrug-targets identification, which is summarized in FIG. 1, which willbe discussed in more detail further below. This approach is based onthree major aspects:

(I) a translational approach based on the initial identification ofcandidate biomarkers and/or drug-targets, in vivo using a definedgenetic mouse model and subsequent validation in human clinical samples;(II) cutting edge mass spectrometry-based methodologies andbioinformatics methods established in our lab for the isolation,identification and quantitation of N-linked glycoproteins followed by(III) multivariate statistical methods to uncover particular signaturesfor the detection of patients with prostate cancer.

According to a first preferred embodiment of the proposed method, it isapplied to the diagnosis of prostate cancer. To this end, the canceroussample proteomes are selected to be sample proteomes of individuals withprostate cancer. Furthermore the tissue samples are prostate tissuesamples, wherein these can be samples with localized or non-localizedprostate cancer. Correspondingly the derived protein/peptide biomarkersare selected to be diagnostic of prostate cancer, can be used for thetherapy of prostate cancer or for the monitoring of the therapy ofprostate cancer.

According to a further embodiment of the proposed method, in step (a)proteins derived from the sample proteomes are selected to beexclusively glycoproteins, preferably N-linked glycoproteins, as theseconstitute a sub proteome which is highly relevant in the context ofcancer drug-target and biomarker discovery.

Preferably in a first step of this step (a) the proteome of thecorresponding sample is digested, preferably by using trypsin and/or LysC (other digestive systems however being possible), and subsequentlyextracted using solid-phase extraction (preferably using the method SPEGas will be discussed in more detail below). The determined biomarkersare correspondingly preferred to be N-linked glycoproteins and/orpeptide fragments thereof.

In principle it would be possible to use cell culture systems at leastfor step (a) and specifically for the tissue samples thereof. However,in order to mimic more closely the complexity of a human disease ordysfunction it is preferred to select the samples to be derived from invivo sources, and most preferably the non-human mammalian individualsare selected to be mice, and preferably a murine prostate tissue for thesamples in step (a) is perfused for complete removal of blood from theprostate tissue prior to the analysis and/or further treatment of theproteome (in case of other diseases or dysfunctions the correspondingtissue or organ can be treated analogously).

As already pointed out above, for several reasons animal models arepreferred. The three main points for this preference are as follows:

-   -   the samples are homogeneous, i.e. the individuals from which the        samples originate are genetically identical and have the same        lesion    -   the lesion corresponds to a lesion observed in human cancer and        thus accurately models tumor development    -   reproducible: very similar samples can be prepared over and over        which is not possible in humans    -   defined perturbation. In humans we have no control over the        perturbations that lead to cancer. In animal models single or a        combination of perturbations can be applied in a tissue specific        and time specific manner.

After the mere identification of proteins/fragments thereof within step(a), preferably only those proteins/fragments thereof selected whichshow a well distinguishable differential behaviour between healthy andcancerous sample sources. To this end, the differential behaviour of themeasured signals (differential abundance) is observed and only thosesignals (corresponding to specific protein/fragments thereof) whichshowed sufficient differential behaviour will be selected for the nextstep for further evaluation.

Differential behaviour can either be a situation, in which a specificsignal is sufficiently increased/decreased when comparing the healthywith the cancerous samples signals, it can however also be a situation,in which there is no signal in the cancerous or the healthy samplesignals, and a clearly detectable signal in the healthy or the canceroussample signals, respectively. According to a preferred embodimenttherefore, the selection criteria for the determination of the presenceof sufficient differential behaviour in step (a) are selected from thefollowing group:

biomarkers regulated in prostate tissue and serum; potential biomarkersregulated in prostate tissue and detected in serum; potential biomarkersregulated in prostate tissue and secreted; potential biomarkersexclusively detected in prostate tissue and sera of mice with cancer;potential biomarkers, specific for prostate and regulated in cancertissue or serum; potential biomarkers specific for prostate andsecreted; potential biomarkers highly regulated in prostate tissue orserum, preferably by a factor of more than four; potential biomarkers,prior knowledge-based selection, preferably characterised by knownbiological function during cancer progression; or a combination thereof,preferably a combination of at least five or most preferably of all ofthese criteria is used. Of these preferably specifically the followingcombination of criteria leads to biomarker systems which can finally beused for human diagnosis/therapy: biomarkers regulated in prostatetissue and serum; potential biomarkers regulated in prostate tissue anddetected in serum; potential biomarkers regulated in prostate tissue andsecreted; potential biomarkers highly regulated in prostate tissue orserum, preferably by a factor of more than four; potential biomarkers,prior knowledge-based selection, preferably characterised by knownbiological function during cancer progression.

Preferably selection takes place (selection meaning that thecorresponding protein/fragment thereof (meaning protein or a fragment ofsuch a protein) will enter the next step) if the factor between signalsof healthy and signals of cancerous samples is either larger than 1.5 orsmaller than 0.75. This in particular applies to the first threeabove-mentioned selection criteria.

Typically, in step (a) the proteins/peptides of the digested proteins ofthe samples are in a first step identified by using a (shotgun) massspectrometric technique, and in a second step a combined liquidchromatography/mass spectrometry technique, preferably a label-freequantitation technique, is used for the identification of thedifferential properties (normally differential abundance) betweenhealthy and cancerous samples. Preferably, within step (a) the massspectrometrically detected proteins/protein fragment signals areattributed to the corresponding proteins by using database informationattributing mass spectrometric signals to specific proteins/proteinfragments.

According to a further preferred embodiment, in step (b) absolutequantification is achieved by using a quantitative internal standard,preferably a specifically synthesised internal standard.

It is further preferred to use in step (b) and/or in step (c) tandemmass spectrometry techniques, preferably selected reaction monitoring(SRM), preferably in combination with liquid chromatography, as massspectrometry method. As concerns these techniques and their definitionsand parameters, for keeping the present specification within reasonableboundaries, reference is made to the publication B. Domon and R.Aebersold, entitled Mass Spectrometry and Protein Analysis (Science 312,121 (2006)) and the corresponding references cited therein. Thedisclosure of these documents is expressly included into thisspecification as concerns these analytical tools for the analysis of theproteome.

The present invention furthermore relates to a cancer diagnosticbiomarker assay and/or therapeutic target which can be determined usinga method as outlined above, or specifically determined using such amethod. Specifically such a biomarker assay and/or therapeutic targetmay consist of the set as outlined further below in the context of thedescription of the corresponding methods, so for example a cancerdiagnostic/therapeutic biomarker assay for localized prostate cancer canbe based on, for the monitoring of localized prostate cancer, inparticular for the distinction from benign prostate hyperplasia, acombined measurement of the concentration of at least two, preferably atleast three proteins and/or fragments of proteins selected from thegroup derived from: ASPN; VTN; AOC3; LOX; PGCP; PSAP; THBS1; CFH; CLU;KIT; TFRC; LGALS3BP; GOLPH2; HYOU1; CTSD; OLFM4; AKAP13; CP; CPE; CPM;ICAM1; MSMB; TM9SF3; GALNTL4 in human serum, plasma or a derivative ofblood, or blood itself. Gene names as given here, Entry names, Proteinnames (shortened) and Accession numbers as generally used in all thisspecification are as defined according to the UniProt Consortium(www.uniprot.org), which is comprised of the European BioinformaticsInstitute (EBI), the Swiss Institute of Bioinformatics (SIB), and theProtein Information Resource (PIR). Preferably the measurement iscarried out using tandem mass spectrometry techniques, preferablyselected reaction monitoring (SRM), more preferably in combination withliquid chromatography, and/or Enzyme-Linked Immunosorbent Assays (ELISA)for the detection of these proteins/fragments thereof.

The present invention furthermore relates to a cancer diagnosticbiomarker assay and/or therapeutic target which can be determined usinga method as outlined above, or specifically determined using such amethod. Specifically such a biomarker assay and/or therapeutic targetmay consist of the set as outlined further below in the context of thedescription of the corresponding methods, so for example a cancerdiagnostic/therapeutic biomarker assay for localized prostate cancer canbe based on ASPN, and optionally VTN, in combination with one of AOC3;LOX; PGCP; PSAP; THBS1; CFH; CLU; KIT; TFRC; LGALS3BP; GOLPH2, HYOU1;CTSD; OLFM4 derived proteins/fragments thereof.

Furthermore the present invention relates to a cancerdiagnostic/therapeutic biomarker assay for the diagnosis, therapy and/orthe therapeutic monitoring of (human) diseases or dysfunctions,preferably of cancer, and most preferably of prostate cancer (localizedor non-localized) comprising the measurement of at least two, preferablyat least three or at least five protein/peptide biomarkers (as forexample determined according to a method as given above) in human serum,plasma or any other derivatives of blood, or blood itself. The assay canfor example be an antibody-based assay such as an Enzyme-LinkedImmunosorbent Assay, it can however also be an LC-SRM assay.

To increase the reliability of such cancer diagnostic biomarker assay,it can be combined with an affinity reagent-based assay, e.g. anantibody-based assay such as Enzyme-Linked Immunosorbent Assay (ELISA)for the detection of further systems such as Prostate Specific Antigen(PSA). Also multiplexing techniques of a series of antibodies forexample using bead techniques are possible in this respect.

The present invention furthermore relates to a method for the diagnosisof localized prostate cancer using a (preferably combined) measurementof the concentration of ASPN derived protein/fragments thereof as wellas VTN derived protein/fragments thereof in human serum, plasma or anyother derivatives of blood, or blood itself. For increasing theaccuracy, it is preferred to carry out one (or even several) furthermeasurements, namely the measurement of one further protein/fragmentsthereof selected from the group derived from: AOC3; LOX; PGCP; PSAP;THBS1. If combined with a PSA-measurement, the further protein/fragmentsthereof can additionally be selected from: CFH; CLU; KIT; TFRC;LGALS3BP; GOLPH2.

Preferably in such a method the measurement is carried out using tandemmass spectrometry techniques, preferably selected reaction monitoring(SRM), typically in combination with preceding liquid chromatography.Alternatively or additionally it is possible to use an antibody basedassay such as an Enzyme-Linked Immunosorbent Assays (ELISA) for thedetection of these proteins/fragments thereof. Combined approaches arepossible, so for example one system (or group of systems) can bedetermined using SRM (if for example no ELISA is available), and theremaining system(s) can be determined by using antibody based techniquessuch as ELISA-techniques.

In such a method, typically for a positive diagnosis of localizedprostate cancer

the concentration of ASPN derived protein/fragments thereof has to bemore than 55 ng/ml, preferably more than 60 ng/ml, and optionally at thesame time,the concentration of VTN derived protein/fragments has to be less than3500 ng/ml, preferably less than 3300 ng/ml.

If, as preferred, additionally one of the above-mentioned additionalsystems is measured,

the concentration of AOC3 derived protein/fragments thereof has to beless than 250 ng/ml, preferably less than 220 ng/ml,and/or the concentration of LOX derived protein/fragments thereof has tobe less than 580 ng/ml, preferably less than 550 ng/ml,and/or the concentration of PGCP derived protein/fragments thereof hasto be more than 550 ng/ml, preferably more than 570 ng/ml,and/or the concentration of PSAP derived protein/fragments thereof hasto be less than 33000 ng/ml, preferably less than 32500 ng/ml, mostpreferably less than 32250 ng/ml,and/or the concentration of THBS1 derived protein/fragments thereof hasto be more than 12500 ng/ml, preferably more than 13000 ng/ml, mostpreferably more than 13500 ng/mland/or the concentration of LGALS3BP derived protein/fragments thereofhas to be more than 390 ng/ml, preferably more than 400 ng/mland/or the concentration of GOLPH2 derived protein/fragments thereof hasto be more than 80 ng/ml, preferably more than 90 ng/mland/or the concentration of HYOU1 derived protein/fragments thereof hasto be more than 35 ng/ml, preferably more than 40 ng/ml,and/or the concentration of CTSD derived protein/fragments thereof hasto be less than 32 ng/ml, preferably less than 25 ng/ml,and/or the concentration of OLFM4 derived protein/fragments thereof hasto be less than 20 ng/ml, preferably less than 15 ng/ml.

Preferably in such a method the measurement is carried out for thediagnosis and/or for the therapy and/or for the monitoring of localizedprostate cancer for the distinction from benign prostate hyperplasia,using a combined measurement of the concentration of at least threeproteins and/or fragments of proteins selected from the group derivedfrom: ASPN; HYOU1; CTSD; OLFM4; in human serum, plasma or a derivativeof blood, or blood itself. For the diagnosis/monitoring preferablyadditionally the concentration of the Prostate Specific Antigen (PSA) inthe human serum, plasma or a derivative of blood, or blood itself ismeasured using an affinity reagent-based, preferably an antibody-basedassay such as an Enzyme-Linked Immunosorbent Assay (ELISA). Furtherpreferably for a positive diagnosis the concentration of the ProstateSpecific Antigen has to be more than 2 ng/ml, preferably more than 4ng/ml.

In this context, preferably for a positive diagnosis or the monitoringof localized prostate cancer the concentration of ASPN derivedprotein/fragments thereof has to be more than 55 ng/ml, preferably morethan 60 ng/ml; and/or the concentration of HYOU1 derivedprotein/fragments thereof has to be more than 35 ng/ml, preferably morethan 40 ng/ml; and/or the concentration of CTSD derivedprotein/fragments thereof has to be less than 32 ng/ml, preferably lessthan 25 ng/ml; and/or the concentration of OLFM4 derivedprotein/fragments thereof has to be less than 20 ng/ml, preferably lessthan 15 ng/ml.

It should be noted in the context of the threshold concentrations asgiven above as well as a detailed further below that these may depend onthe specific measurement technique, as for example the methods usedhere, namely SRM, will measure the total species, so e.g. free and boundspecies, while for example an antibody-based assay such as ELISA mightbe able to distinguish between these two forms leading to differentthreshold concentrations if the latter methods are used. The valuesgiven here therefore in particular relate to measurements usingSRM-methods, and they might have to be adapted by analogy if differentmethods are being used. This is however a matter of conversion which iswithin the realm of the skills of the person skilled in the art in thisfield.

The present invention furthermore relates to an extremely high accuracymethod for the diagnosis of metastatic prostate cancer using a(preferably combined) measurement of the concentration of ASPN and CTSDand THBS1 and GALNTL4 as well as VTN derived protein/fragments thereofin human serum, plasma or any other derivatives of blood, or blooditself, preferably in combination with the measurement of one furtherprotein/fragments thereof selected from the group derived from: PSAP;GSPT1; CEACAM1; HYOU1; EFNA5; KIT.

Preferably, as in the above case of the methods for diagnosis oflocalized prostate cancer, the measurement is carried out using tandemmass spectrometry techniques, preferably selected reaction monitoring(SRM), more preferably in combination with liquid chromatography, and/orantibody based methods such as Enzyme-Linked Immunosorbent Assays(ELISA) for the detection of these proteins/fragments thereof.

For a positive diagnosis of non-localized (metastatic) prostate cancer

the concentration of ASPN derived protein/fragments thereof has to bemore than 60 ng/ml, preferably more than 65 ng/ml, most preferably morethan 68 ng/ml and at the same timethe concentration of CTSD derived protein/fragments has to be more than120 ng/ml, preferably more than 130 ng/ml, most preferably more than 133ng/ml and at the same timethe concentration of THBS1 derived protein/fragments has to be less than12000 ng/ml, preferably less than 11500 ng/ml, most preferably less than10750 ng/ml and at the same timethe concentration of GALNTL4 derived protein/fragments has to be morethan 1400 ng/ml, preferably more than 1600 ng/ml, most preferably morethan 1650 ng/ml and at the same timethe concentration of VTN derived protein/fragments has to be more than3000 ng/ml, preferably more than 3150 ng/ml, most preferably more than3300 ng/ml.

If, as preferred, additionally one of the above-mentioned additionalsystems is measured,

the concentration of PSAP derived protein/fragments thereof has to bemore than 33000 ng/ml, preferably more than 34000 ng/ml,and/or the concentration of GSPT1 derived protein/fragments thereof hasto be more than 450 ng/ml, preferably more than 500 ng/ml, morepreferably more than 510 ng/ml,and/or the concentration of CEACAM1 derived protein/fragments thereofhas to be more than 35 ng/ml preferably more than 38 ng/ml, (thisthreshold value being the only one calculated in relation of ELISAdetermination)and/or the concentration of HYOU1 derived protein/fragments thereof hasto be more than 80 ng/ml, preferably more than 89 ng/ml,and/or the concentration of EFNA5 derived protein/fragments thereof hasto be more than 60 ng/ml, preferably more than 65 ng/ml,and/or the concentration of KIT derived protein/fragments thereof has tobe more than 90 ng/ml, preferably more than 95 ng/ml.

As mentioned above, it can be advantageous to combine the measurement ofthe above-mentioned systems, be it for localized or non-localizedprostate cancer diagnosis, with the measurement of further parameters ofthe serum, plasma or any other derivatives of blood, or blood itselfwhich are not the result of a biomarker determination method as outlinedabove. It is for example possible that for the diagnosis additionallythe concentration of the Prostate Specific Antigen (PSA) in the humanserum, plasma or any other derivatives of blood, or blood itself ismeasured using a corresponding antibody-based assay such as anEnzyme-Linked Immunosorbent Assay (ELISA), wherein for a positivediagnosis the concentration of the Prostate Specific Antigen (PSA)normally has to be more than 2 ng/ml, preferably more than 4 ng/ml.

Further embodiments of the present invention are outlined in thedependent claims.

SHORT DESCRIPTION OF THE FIGURES

In the accompanying drawings preferred embodiments of the invention areshown in which:

FIG. 1 is an overview of the integrated proteomic approach for biomarkerdiscovery, verification and validation. The scheme is divided in twomain sections: First the discovery and verification phases performedusing an animal model and second the validation phase with human patientsamples; the numbers in italics indicate the number of glycoproteinsthat were identified and considered for the next step; wherein in a):selective enrichment of N-glycopeptides was performed from tissue andserum from healthy and cancerous mice to discover in vivo CaP-specificsignatures using prostate tissue from a mouse model of CaP; this allowsto create a catalogue of 785 glycoproteins which served as a resourcefor the later steps; MS-based label-free quantification was performed onthe same murine tissue and serum samples; this resulted in a relativequantification of 352 glycoproteins comparing cancerous vs. benignsamples; 164 glycoproteins matching criteria were then chosen forfurther investigation; wherein b): 41 of these biomarker candidatescould be validated in sera of mice and wherein c): 43 candidates inhuman patients by MS based selected reaction monitoring (SRM) and ELISA;generally, the boundaries between the human and animal steps areflexible; e.g. it is possible to do the verification step also in humansamples provided such a collection is actually available;

FIG. 2 shows an overview of the Mouse Glycoproteome Catalog, wherein thenumber of proteins identified in the mouse prostate tissue and serum areshown as a Venn diagram; the number of proteins that could be quantifiedis shown below; and

FIG. 3 shows the discriminant accuracy of selected candidates inmultivariate approaches; a patient is classified following a rulegenerated by the statistical software; the % of correct predictions isdefined as accuracy of the model; as indicated above, Gene names are asdefined according to the UniProt Consortium (www.uniprot.org), which iscomprised of the European Bioinformatics Institute (EBI), the SwissInstitute of Bioinformatics (SIB), and the Protein Information Resource(PIR); the shaded entries in the first lines indicate which systems canbe interchanged within one assay.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Referring to the drawings, which are for the purpose of illustrating thepresent preferred embodiments of the invention and not for the purposeof limiting the same, FIG. 1 shows an overview of the integratedproteomic approach for biomarker discovery, verification and validation.The scheme is divided in two main sections: First the discovery andverification phases (a) and (b) performed using an animal model (mice)and second the validation phase (c) with human patient samples.

In the following example the method is applied to the determination ofbiomarkers for prostate cancer. As outlined above, this shall howevernot be construed to the actual gist of the invention, as the method mayequivalently be applied to other types of cancer such as breast cancer,lung cancer, ovarian cancer and the like and it may also equivalently beapplied generally to other types of diseases or dysfunctions such asdiabetes (mellitus and other types), neurodegenerative diseases: such asAlzheimer's disease, Parkinson's disease, Huntington's disease,Creutzfeldt-Jakob disease; autoimmune diseases: such as multiplesclerosis, rheumatoid arthritis; infectious diseases: such as malaria,HIV; cardiovascular disease: such as hypertension, atherosclerosis. Asoutlined above, the main strength of the animal model work is thatspecific, defined perturbations can be applied and that the consequencesof these are being measured. The same perturbations can be also berelevant to other types of cancers, which means that it is possible tolook at the markers as a rationale consequence of the inducedperturbation as opposed to what the general term like disease relatedmight suggest.

In the initial discovery phase, prostate tissue samples, serum samplesand both on the one hand of healthy mice and of prostate cancerous micewere used, so four different series of experiments. For thedetermination of the differential behaviour only tissue samples ofhealthy/cancerous mice were compared, and on the other hand serumsamples of healthy/cancerous mice were compared.

In a first step (a) the tissue (prepared as described in more detailbelow) and the serum samples were digested using trypsin, and from thecorresponding proteome digests the N-linked glycosylated proteinfragments were selected and extracted using the SPEG-technique(described in more detail below).

Subsequently glycoprotein identification was carried out using massspectrometry, specifically a shotgun approach, without determiningdifferential behaviour in this stage. This resulted in a total of 532detected glycoproteins in prostate tissue and 253 detected glycoproteinsin serum. A total of 785 glycoproteins were detected, as 110 proteinswere detected in the tissue as well as in the serum (graphicallyillustrated in FIG. 2).

The next step designated with label-free quantitation aims at thedetection of the differential behaviour of the signals of the proteinfragments. The experiment is a combined liquid chromatography/massspectrometry experiment in which mass spectrometry is carried outaccording to the elution profile of the chromatography. Using thisexperiment one can track the differential behaviour betweenhealthy/cancerous samples. Those signals/protein fragments which in thislabel-free quantitation step did not show differential behaviour wererejected from the above-mentioned set of 785 glycoproteins, leading to352 quantified proteins, of which 279 originate from the tissue samplesand 160 from the serum samples (illustrated in FIG. 2).

These 352 quantified proteins are now further selected to only keepthose which show a pronounced differential behaviour, and which complywith at least one of the eight rationales as given and discussed in thecontext of table 1 below. This after the filtering using the rationalesleads to 164 potential candidate biomarker systems, which are resultingfrom the attribution of the signals to the specific glycoproteins byusing electronic annotation.

In the second step (b), which is optional, verification or ratherqualification using the nonhuman system takes place, wherein the finalanalytical tools which are to be used for the final biomarker assaymethod are used. In this step (b) correspondingly only serum ofhealthy/cancerous mice is analysed, it is again digested and theglycoproteins extracted as described in the context of (a), butsubsequently selected reaction monitoring (SRM), i.e. a liquidchromatography/tandem mass spectrometry method is used for absolutequantitation of the systems using specifically provided (synthesised)internal standards for absolute quantitation.

Out of the 164 systems which have entered step (b) only 41 could beabsolutely quantified mainly for practical reasons. The corresponding 41systems are given in table 2 discussed in more detail below.

Therefore for the next step all the 164 systems having resulted fromstep (a) are used for the final step of validation (c). The results ofstep (b) are further verified by using RT-PCR, immunohistochemistry,western blot.

Within step (c) essentially the same procedure is carried out as withinstep (b) however this time using serum samples of human origin ofhealthy/cancerous individuals. From the SRM-side this leads to 37candidates. Wherever possible, the 164 candidates having entered step(c) are furthermore validated using available ELISA assays, leading toan additional 11 possible candidates.

Due to the fact that certain systems result from the SRM verification aswell as from the ELISA validation, this results in a final number of 43candidate biomarker systems. These are listed in table 3.

From a principal point of view any of these, possibly in combinationwith one or several, can be used in an assay for the detection ofprostate cancer.

In view of reducing the number of necessary measurements by at the sametime keeping an as high as possible accuracy, statistical methods (for amore detailed discussion see further below) were applied to all 43systems in correlation with the patient's data collection leading to thefinal assays as given in FIG. 3.

Five particularly high accuracy assays are given in FIG. 3 A), and onenotes that in all of them ASPN as well as VTN are present.Correspondingly therefore glycoproteins derived from these genes orrather the fragments of these glycoproteins are highly indicative forthe distinction between benign prostate hyperplasia (BPH) and localizedprostatic cancer (locPCa). The corresponding accuracies are above 80%,so roughly around 20% higher than the accuracy of present state of theart PSA-methods.

Using additional incorporation of PSA-measurements using ELISA, onestatistically finds further nine biomarker assays for discriminatingbetween BPH and locPCa as given in FIG. 3 B). Again in all of thesesystems ASPN as well as VTN derived glycoproteins are present. Theaccuracies of these combined measurements are another 10% higher thanwithout taking PSA-measurements into account, leading to a so farunreached exceedingly high accuracy for the detection of prostatecancer.

Using additional incorporation of PSA-measurements using ELISA and moredata, one statistically finds further 5 biomarker assays fordiscriminating between BPH and locPCa as given in FIG. 3 C). In each ofthese systems three out of the systems of the group: ASPN, OLFM4, HYOU1,CTSD derived glycoproteins are present. The corresponding accuracies areagain above 80%, so roughly around 20% higher than the accuracy ofpresent state of the art PSA-methods.

Finally in FIG. 3 D) the statistical results for biomarker assays forthe discrimination between locPCa and metCPa is given. Using thecombined measurements of six systems in each assay one reaches 100%accuracy.

The above shows that the proposed method not only provides a powerfultool for targeted development of biomarker assays with high accuracy. Itfurthermore shows that for the specific situation of prostate cancer thecorrespondingly determined biomarker assays show an unexpectedly highaccuracy which exceeds anything so far reported in the literature.

EXPERIMENTAL DETAILS

(I) Translational approach: The translational approach is based on theinitial identification of interesting candidate biomarkers in a mousemodel for prostate cancer and the validation of such candidates in humanclinical samples. To identify candidate biomarkers to be used in theclinics as defined diagnostic or therapeutical targets, we started toanalyze the prostate tissue and blood from genetically defined mice thatdevelop prostate cancer (Pten conditional knockout, cKO, see e.g. US2006/0064768) and control mice that have intact Pten alleles and do notdevelop such tumors.

Rationales for using a mouse model: We decided to use the geneticallydefined Pten conditional knockout model because these mice develop earlystage epithelial prostate cancers following deletion of the tumorsuppressor gene Pten. The phenotype is closely related to humanlocalized prostate cancer and is thus an ideal starting point for theidentification of novel biomarkers that could distinguish humanlocalized prostate cancer from benign hyperplastic lesions (BenignProstatic Hyperplasia or BPH). Moreover, the use of a Pten cKO mousemodel allows to identify therapeutical/imaging targets and biomarkers tobe used specifically for stratified patients having PTEN mutations orany imbalance derived by mutations along the PTEN signaling pathway. Theuse of a mouse model facilitates the initial identification of candidatebiomarkers since the prostate tissue is very homogeneous and majorvariables such as environmental conditions and timing can be controlled,in contrast to the highly heterogeneous human tissues. Interestingly,the ratio between prostate cancer tissue volume and total blood volumeis 40-40000× higher in mice compared to men. This is of course anintrinsic advantage since variations in the blood proteome are expectedto be better uncovered in such a model than in human patient samples.Finally only the murine tissue can be efficiently perfused in order toeliminate blood contaminations (see below). Blood protein contaminationsin the tissues often mask the identification of proteins present atparticular low concentration. Moreover, the absence of blood in thetissue following perfusion allows to apply comparative proteomics(blood-tissue) without any potential bias (see table 1, rationales1,2,4,7).

TABLE 1 Selection of interesting proteins for validation in human SeraDiscriminant List of rationales: factor 1 potential biomarkers regulatedin prostate tissue AND serum >1.5 or <0.75 2 potential biomarkersregulated in prostate tissue AND detected >1.5 or <0.75 in serum 3potential biomarkers regulated in prostate tissue AND secreted >1.5 or<0.75 4 potential biomarkers exclusively detected in prostate tissue ANDsera of mice with cancer 5 potential biomarkers, specific for prostateAND regulated in cancer tissue or serum 6 potential biomarkers specificfor prostate AND secreted 7 potential biomarkers, top regulated inprostate tissue or serum (>4x) 8 potential biomarkers, priorknowledge-based selection (biological function during cancerprogression) annotated or predicted cellular Ratio- Gene Accessionlocalization nale name Entry name Protein name number (ref 6) 1 1 Ecm1ECM1_MOUSE Extracellular matrix protein 1 Q61508 secreted 2 1 EgfrEGFR_MOUSE Epidermal growth factor Q01279 plasma membrane/ receptorsecreted 3 1 Trf TRFE_MOUSE Serotransferrin Q921I1 secreted 4 1 Pdia6PDIA6_MOUSE Protein disulfide-isomerase Q922R8 ER A6 5 1 Hsp90b1ENPL_MOUSE Endoplasmin P08113 ER 6 1 Rnase1 RNAS1_MOUSE Ribonucleasepancreatic P00683 secreted 7 1 Lifr LIFR_MOUSE Leukemia inhibitoryfactor P42703 plasma membrane receptor 8 2 Ighg1 IGH1M_MOUSE Ig gamma-1chain C region, P01869 secreted membrane-bound form 9 2 Clu CLUS_MOUSEClusterin Q06890 secreted 10 2 Cfh CFAH_MOUSE Complement factor H P06909secreted 11 2 H2-L HA1L_MOUSE H-2 class I histocompatibility P01897plasma membrane antigen, L-D alpha chain 12 2 Col12a1 COCA1_MOUSECollagen alpha-1(XII) chain Q60847 secreted 13 2 Dpp7 DPP2_MOUSEDipeptidyl-peptidase 2 Q9ET22 lysosomal 14 2 Pgcp O70216_MOUSE Plasmaglutamate Q9WVJ3 secreted? carboxypeptidase 15 2 Cp CERU_MOUSECeruloplasmin Q61147 secreted 16 2 Cfb CFAB_MOUSE Complement factor BP04186 secreted 17 2 Lrp1 LRP1_MOUSE Low-density lipoprotein Q91ZX7secreted receptor-related protein 1 18 2 Col1a1 CO1A1_MOUSE Collagenalpha-1(I) chain P11087 secreted 19 2 Itgav ITAV_MOUSE Integrin alpha-VP43406 plasma membrane 20 2 Lama3 LAMA3_MOUSE Laminin subunit alpha-3Q61789 secreted 21 2 Fn1 FINC_MOUSE Fibronectin P11276 secreted 22 2Anpep AMPN_MOUSE Aminopeptidase N P97449 plasma membrane 23 2 CtseCATE_MOUSE Cathepsin E P70269 endosomal 24 2 Ctsa PPGB_MOUSE Lysosomalprotective protein P16675 lysosomal 25 2 Ceacam1 CEAM1_MOUSECarcinoembryonic antigen- P31809 plasma membrane related cell adhesionmolecule 1 26 2 Ace ACET_MOUSE Angiotensin-converting P22967 plasmamembrane/ enzyme, testis-specific secreted isoform 27 2 Pdia3PDIA3_MOUSE Protein disulfide-isomerase P27773 ER A3 28 2 Sslp1SSLP1_MOUSE Secreted seminal-vesicle Ly- Q3UN54 secreted 6 protein 1 292 Btd BTD_MOUSE Biotinidase Q8CIF4 secreted 30 2 Atp1b2 AT1B2_MOUSESodium/potassium- P14231 plasma membrane transporting ATPase subunitbeta-2 31 2 Hspa5 GRP78_MOUSE 78 kDa glucose-regulated P20029 ER protein32 2 Psap SAP_MOUSE Sulfated glycoprotein 1 Q61207 secreted 33 2 Thbs1TSP1_MOUSE Thrombospondin 1 P35441 secreted 34 2 Adcy3 ADCY3_MOUSEAdenylate cyclase type 3 Q8VHH7 plasma membrane 35 2 Ctbs DIAC_MOUSEDi-N-acetylchitobiase Q8R242 lysosomal 36 2 Ggh GGH_MOUSE Gamma-glutamylhydrolase Q9Z0L8 secreted/lysosomal 37 2 Serping1 IC1_MOUSE Plasmaprotease C1 inhibitor P97290 secreted/plasma? 38 2 L1cam L1CAM_MOUSENeural cell adhesion P11627 plasma membrane molecule L1 39 2 1100001PLBL1_MOUSE Putative phospholipase B- Q8VCI0 secreted H23Rik like 1 40 2Qsox1 QSOX1_MOUSE Sulfhydryl oxidase 1 Q8BND5 secreted/Golgi membrane 412 Lrg1 Q91XL1_MOUSE Leucine-rich alpha-2- Q91XL1 secreted/plasma?glycoprotein 42 2 Lgals3bp O35649_MOUSE Cyclophilin C-associated O35649plasma membrane protein 43 2 Cd44 CD44_MOUSE CD44 antigen P15379 plasmamembrane 44 3 Col14a1 COEA1_MOUSE Collagen alpha-1(XIV) chain Q80X19secreted 45 3 Fam3d FAM3D_MOUSE Protein FAM3D P97805 secreted 46 3 Pon3PON3_MOUSE Serum paraoxonase/ Q62087 secreted lactonase 3 47 3 Timp1TIMP1_MOUSE Metalloproteinase inhibitor 1 P12032 secreted 48 3 Abca16Q6XBG1_MOUSE ATP-binding cassette Q6XBG1 plasma? Membrane/ transportersub-family A secreted? member 16 49 3 Fbn1 FBN1_MOUSE Fibrillin-1 Q61554secreted 50 3 Lum LUM_MOUSE Lumican P51885 secreted 51 3 Lamb2LAMB2_MOUSE Laminin subunit beta-2 Q61292 secreted 52 3 Vcan CSPG2_MOUSEVersican core protein Q62059 secreted 53 3 Bgn PGS1_MOUSE BiglycanP28653 secreted 54 3 Enpp5 ENPP5_MOUSE Ectonucleotide Q9EQG7 secretedpyrophosphatase/phosphodi- esterase family member 5 55 3 Erap1ERAP1_MOUSE Endoplasmatic reticulum Q9EQH2 secreted aminopeptidase 1 563 Pxdn PXDN_MOUSE Peroxidasin homolog Q3UQ28 secreted/ER 57 3 Col6a3O88493_MOUSE Type VI collagen alpha 3 O88493 secreted subunit 58 3Emilin1 EMIL1_MOUSE EMILIN-1 Q99K41 secreted 59 3 Mfap4 MFAP4_MOUSEMicrofibril-associated Q9D1H9 secreted glycoprotein 4 60 3 AgrnO08860_MOUSE Agrin O08860 secreted 61 3 Prelp PRELP_MOUSE ProlarginQ9JK53 secreted 62 3 Lamc1 LAMC1_MOUSE Laminin subunit gamma-1 P02468secreted 63 3 Lama1 LAMA1_MOUSE Laminin subunit alpha-1 P19137 secreted64 3 Lama5 LAMA5_MOUSE Laminin subunit alpha-5 Q61001 secreted 65 3Lama2 LAMA2_MOUSE Laminin subunit alpha-2 Q60675 secreted 66 3 Col6a5A6H586_MOUSE Collagen type VI alpha 5 A6H586 secreted 67 3 Lamb1-1LAMB1_MOUSE Laminin subunit beta-1 P02469 secreted 68 3 Creg1CREG1_MOUSE Protein CREG1 O88668 secreted 69 3 Sva Q64367_MOUSE Seminalvesicle autoantigen Q64367 secreted 70 3 Serpinb6 SPB6_MOUSE Serpin B6Q60854 plasma membrane?/ secreted? 71 3 Cpe CBPE_MOUSE CarboxypeptidaseE Q00493 secretory granules 72 3 9530002 SPIKL_MOUSE Serine proteaseinhibitor Q8CEK3 secreted K18Rik kazal-like protein 73 3 Olfm4OLFM4_MOUSE Olfactomedin-4 Q3UZZ4 secreted 74 3 Lama4 LAMA4_MOUSELaminin subunit alpha-4 P97927 secreted 75 3 Fcgbp A1L0S2_MOUSELOC100037259 protein A1L0S2 secreted/ER?/ golgi? 76 3 Dmbt1 DMBT1_MOUSEDeleted in malignant brain Q60997 secreted/plasma tumors 1 proteinmembrane 77 3 Wfdc3 Q14AE4_MOUSE Wap four-disulfide core Q14AE4 secreteddomain 3 78 3 Spink5 Q5K5D4_MOUSE Spink5 protein Q5K5D4 secreted 79 3Ngp Q61903_MOUSE Myeloid secondary granule Q61903 secreted protein 80 3Col7a1 CO7A1_MOUSE Collagen alpha-1(VII) chain Q63870 secreted 81 3Itih5 ITIH5_MOUSE Inter-alpha-trypsin inhibitor Q8BJD1 secreted heavychain H5 82 3 Hyal6 Q8CDQ9_MOUSE Hypothetical Glycoside Q8CDQ9 secreted?hydrolase family 56 containing protein 83 3 BC023744 Q0P6B3_MOUSEBC023744 protein Q0P6B3 secreted 84 3 Aspn ASPN_MOUSE Asporin Q99MQ4secreted 85 4 Postn POSTN_MOUSE Periostin Q62009 secreted 86 4 Fmr1FMR1_MOUSE Fragile X mental retardation P35922 secreted?/ protein 1homolog cytoplasmic 87 4 Golga5 GOGA5_MOUSE Golgin subfamily A member 5Q9QYE6 Golgi 88 4 Grn GRN_MOUSE Granulins P28798 secreted 89 4 Man2b1MA2B1_MOUSE Lysosomal alpha- O09159 lysosomal mannosidase 90 4 Nav1NAV1_MOUSE Neuron navigator 1 Q8CH77 cytoplasmic 91 4 Ramp3 RAMP3_MOUSEReceptor activity-modifying Q9WUP1 plasma membrane protein 3 92 5 Tspan1Q99J59_MOUSE Tetraspanin 1 Q99J59 plasma membrane 93 5 5430419Q8BZE1_MOUSE hypothetical Speract receptor Q8BZE1 plasma membrane D17Rik94 5 Grk5 GRK5_MOUSE G protein-coupled receptor Q8VEB1 cytoplasmickinase 5 95 5 Azgp1 ZA2G_MOUSE Zinc-alpha-2-glycoprotein Q64726 secreted96 6 Spink3 ISK3_MOUSE Serine protease inhibitor P09036 secretedKazal-type 3 97 6 Egf EGF_MOUSE Pro-epidermal growth factor P01132plasma membrane/ secreted 98 6 Msmb MSMB_MOUSE Beta-microseminoproteinO08540 secreted 99 6 Creld2 CREL2_MOUSE Cysteine-rich with EGF-likeQ9CYA0 secreted/ER domain protein 2 100 6 Pbsn PBAS_MOUSE ProbasinO08976 secreted 101 6 Sbp SPBP_MOUSE Prostatic spermine-binding P15501secreted protein 102 7 Ermp1 ERMP1_MOUSE Endoplasmatic reticulum Q3UVK0ER membrane metallopeptidase 1 103 7 Pigr PIGR_MOUSEPolymeric-immunoglobulin O70570 plasma membrane receptor 104 7 Cadm1CADM1_MOUSE Cell adhesion molecule 1 Q8R5M8 plasma membrane 105 7 Golph2GOLM1_MOUSE Golgi phosphoprotein 2 Q91XA2 Golgi 106 7 Tspan8Q8R3G9_MOUSE Tspan8 Q8R3G9 plasma membrane 107 7 Adam3 Q62287_MOUSECyritestin Q62287 plasma membrane 108 7 Thy1 THY1_MOUSE Thy-1 membraneP01831 plasma membrane glycoprotein 109 7 Mme NEP_MOUSE NeprilysinQ61391 plasma membrane 110 7 Apmap APMAP_MOUSE Adipocyte plasma Q9D7N9plasma membrane membrane-associated protein 111 7 Ergic3 ERGI3_MOUSEEndoplasmatic reticulum- Q9CQE7 ER/Golgi Golgi intermediate compartmentprotein 3 112 7 9530003J Q8BM27_MOUSE Weakly similar to Q8BM27 secreted23Rik LYSOZYME C, TYPE M 113 7 Ceacam10 CEAMA_MOUSE Carcinoembryonicantigen- Q61400 secreted related cell adhesion molecule 10 114 7 Plxna3P70208_MOUSE Plexin 3 P70208 plasma membrane 115 7 Vmn2r10 O35204_MOUSEPutative phermone receptor O35204 plasma membrane 116 7 Hyou1HYOU1_MOUSE Hypoxia up-regulated protein Q9JKR6 secreted/ER 1 117 7Defb50 BD50_MOUSE Beta-defensin 50 Q6TU36 secreted 118 7 FcgbpQ8BZG2_MOUSE hypothetical von Willebrand Q8BZG2 secreted factor type Dprotein 119 7 Rai2 RAI2_MOUSE Retinoic acid-induced Q9QVY8 nuclearprotein 2 120 7 Pnliprp1 LIPR1_MOUSE Pancreatic lipase-related Q5BKQ4secreted protein 1 121 7 Pdia2 Q14AV9_MOUSE Pdia2 protein Q14AV9 ERmembrane 122 7 Hp HPT_MOUSE Haptoglobin Q61646 secreted/plasma 123 7 CpmCBPM_MOUSE Carboxypeptidase M Q80V42 plasma membrane 124 7 PigsPIGS_MOUSE GPI transamidase component Q6PD26 ER PIG-S 125 7 Mup3MUP3_MOUSE Major urinary protein 3 P04939 secreted 126 7 Gc VTDB_MOUSEVitamin D-binding protein P21614 secreted 127 7 Prom1 PROM1_MOUSEProminin-1 O54990 plasma membrane 128 7 Vtn VTNC_MOUSE VitronectinP29788 secreted 129 7 Aoc3 AOC3_MOUSE Membrane copper amine O70423plasma membrane oxidase 130 8 Lamp1 LAMP1_MOUSE Lysosome-associatedP11438 lysosomal membrane glycoprotein 1 131 8 Lamp2 LAMP2_MOUSELysosome-associated P17047 lysosomal membrane glycoprotein 2 132 8 Itgb1ITB1_MOUSE Integrin beta-1 P09055 plasma membrane 133 8 Itgae ITAE_MOUSEIntegrin alpha-E Q60677 plasma membrane 134 8 Flt4 VGFR3_MOUSE Vascularendothelial growth P35917 plasma membrane factor receptor 3 135 8 TncTENA_MOUSE Tenascin Q80YX1 secreted 136 8 Fap SEPR_MOUSE Seprase P97321secreted 137 8 Asph Q6P8S1_MOUSE Aspartate-beta-hydroxylase Q6P8S1 ER138 8 Asah1 ASAH1_MOUSE Acid ceramidase Q9WV54 lysosomal 139 8 AtrnATRN_MOUSE Attractin Q9WU60 plasma membrane 140 8 Cacna2d1 CA2D1_MOUSEVoltage-dependent calcium O08532 plasma membrane channel subunit alpha-2/delta-1 141 8 Chl1 CHL1_MOUSE Neural cell adhesion P70232 plasmamembrane/ molecule L1 secreted 142 8 Ctsd CATD_MOUSE Cathepsin D P18242lysosomal 143 8 Dpp4 DPP4_MOUSE Dipeptidyl peptidase 4 P28843 plasmamembrane/ secreted 144 8 Gba GLCM_MOUSE Glucosylceramidase P17439lysosomal 145 8 Ncam1 NCA12_MOUSE Neural cell adhesion P13594 plasmamembrane molecule 1 146 8 Plxnb2 Q3UH76_MOUSE Plexin B2 Q3UH76 plasmamembrane 147 8 Ptprj PTPRJ_MOUSE Protein-type tyrosine-protein Q64455plasma membrane phosphatase eta 148 8 Ptprk PTPRK_MOUSE Receptor-typetyrosine- P35822 plasma membrane protein phosphatase kappa 149 8 SirpaSHPS1_MOUSE Tyrosine-protein phosphatase P97797 plasma membranenon-receptor type substrate 1 150 8 Kit KIT_MOUSE Mast/stem cell growthfactor P05532 plasma membrane receptor 151 8 Sema4d SEM4D_MOUSESemaphorin-4D O09126 plasma membrane 152 8 Apob48r AB48R_MOUSEApolipoprotein B-100 Q8VBT6 plasma membrane receptor 153 8 Agtr1AGTRA_MOUSE Type-1A angiotensin II P29754 plasma membrane receptor 154 8Tm9sf3 TM9S3_MOUSE Transmembrane 9 Q9ET30 plasma membrane? superfamilymember 3 155 8 Galntl4 GLTL4_MOUSE Polypeptide n- Q8K1B9 Golgiacetylgalactosaminyl- transferase 156 8 Efna5 EFNA5_MOUSE Ephrin-a5O08543 plasma membrane 157 8 F5 O88783_MOUSE Coagulation factor V O88783secreted 158 8 Nptn NPTN_MOUSE Neuroplastin P97300 plasma membrane 159 8Lox LYOX_MOUSE Protein-lysine 6-oxidase P28301 secreted 160 8 Mmel1MMEL1_MOUSE Membrane metallo- Q9JLI3 plasma membrane endopeptidase-like1 161 8 Tfrc TFR1_MOUSE Transferrin receptor Q62351 plasma membrane 1628 Gspt1 Q8K2E1_MOUSE G1 to S phase transition 1 Q8K2E1 163 8 Akap13Q3T998_MOUSE A kinase (PRKA) anchor Q3T998 protein 13 164 8 VasnVASN_MOUSE Vasorin Q9CZT5 plasma membrane 165 8 Icam1 ICAM1_MOUSEIntercellular adhesion P13597 plasma membrane molecule 1 Table 1:Selection of interesting proteins for validation in human sera. 165glycoproteins detected in the mouse serum and tissue were selected forverification through targeted mass spectrometry and later validation inhuman clinical samples. Gene names, Entry names, Protein names(shortened) and Accession numbers as generally used in thisspecification are as defined according to the UniProt Consortium(www.uniprot.org), which is comprised of the European BioinformaticsInstitute (EBI), the Swiss Institute of Bioinformatics (SIB), and theProtein Information Resource (PIR). The annotated or predicted cellularlocalization is according to Emanuelsson O, Brunak S, von Heijne G,Nielsen H. (2007) Locating proteins in the cell using TargetP, SignalPand related tools. Nat Protoc. 2, 953-71.

Rationales for using mice and not cell culture systems: Proteomicstechniques are easily applied to cell lines in vitro, whereas the use ofin vivo models requires more complex handling and initialtrouble-shootings. We decided to use an in vivo model however, becausethis mimics more closely the complexity of the human disease compared toin vitro models. The approach presented here is thus unique as very fewscreens today are applied to freshly isolated organs.

Tissue and blood extraction procedure: Mice are anesthetized and bloodis extracted by pinning the left heart ventricle. Mice are subsequentlyheart perfused. This allows for the complete removal of blood from theprostate tissue. Tissue samples are then dissected and pure prostatetissue is readily snap-frozen and pulverized by using a mortar andpestle in the presence of liquid nitrogen. Serum is extracted from theblood and stored at −80° C. until use.

(II) Cutting edge mass-spectrometry (MS) and bioinformatics: Rationalesfor focusing on the N-linked glycoproteome: In order to find candidatebiomarkers, we decided to focus on a particular and highly relevantsubproteome, the N-linked glycosylated proteins. Protein glycosylationhas long been recognized as a common post-translational modification.Typically, glycans are linked to serine or threonine residues (O-linkedglycosylation) or to asparagine residues (N-linked glycosylation).N-linked glycosylation sites generally fall into the NxS/T sequencemotif in which x denotes any amino acid except proline. Theglycosylation of proteins is a characteristic post-translationalmodification of proteins residing in the extracellular space. This meansthat the vast majority of proteins that are specifically secreted orshed by the tumor and released into the bloodstream (which makes themhighly valuable biomarker candidates) are glycosylated. Moreover, theenrichment of glycoproteins enables to unmask interesting candidatespresent at particular low concentration because highly abundant,non-glycosylated and non-relevant proteins such as cytoskeletal proteinsin tissue samples as well as albumin (present at 35-50 mg/ml) in theserum samples are excluded from the measurements.

N-linked glycopeptide extraction procedure and quantification: Toidentify N-linked glycoproteins, we employed a method for the solidphase extraction of N-glycopeptides (SPEG) from tissue and serumaccording to Zhang, H., Li, X. J., Martin, D. B., and Aebersold, R.(2003) Identification and quantification of N-linked glycoproteins usinghydrazide chemistry, stable isotope labeling and mass spectrometry; NatBiotechnol 21, 660-666, the disclosure of which is expressly includedinto the specification as concerns SPEG. Glycopeptides are coupled to asolid support via their glycan moieties. Non-glycosylated peptides arethen washed away and N-glycopeptides can be specifically released usingthe enzyme PNGase F. The method can be applied to tissue and serumalike.

The high mass accuracy and retention time reproducibility of the massspectrometer instrument setup used (LTQ-FT instrument), in combinationwith the trans proteomic pipeline (TPP) software suite and SuperHirn(see e.g. Mueller et al. An Assessment of Software Solutions for theAnalysis of Mass Spectrometry Based Quantitative Proteomics Data. JProteome Res (2008) vol. 7 (1) pp. 51-61), allowed for theidentification and direct label-free quantification of common peptidefeatures. Thereby, peptide elution profiles from different runs werecompared and glycoprotein ratios were calculated from theN-glycopeptides belonging to the same protein. Verification andvalidation phase: In order to verify our findings from the initialdiscovery phase, a list of interesting proteins selected by variousrationales were quantified in the corresponding murine sera by targetedmass spectrometry via selected reaction monitoring (SRM, see e.g.Stahl-Zeng, J., Lange, V., Ossola, R., Eckhardt, K., Krek, W.,Aebersold, R., and Domon, B. (2007) High sensitivity detection of plasmaproteins by multiple reaction monitoring of N-glycosites. Mol CellProteomics 6, 1809-1817.).

This novel approach allows the simultaneous detection and quantificationof proteins comparable in sensitivity to classical immunodetectionprocedures (e.g. Enzyme-Linked ImmunoSorbent Assay, ELISA), but with theadvantage of not requiring tedious optimization steps for each biomarkercandidate and generation of new antibodies. The SRM experiment isaccomplished by specifying the parent mass of the compound for MS/MSfragmentation and then specifically monitoring for a single fragmention. Thus, SRM delivers a unique fragment ion that can be monitored andquantified in the midst of a very complicated matrix. Stable isotopelabeled peptides corresponding to the targeted N-glycosites (A peptidethat was N-glycosylated in the intact protein in its de-glycosylatedform) were synthesized and used as internal standards. This allowed forthe absolute quantification of endogenous glycoproteins present in themice sera (Table 2).

TABLE 2 Glycoproteins measured by SRM in murine sera Gene Accessionp-value p-value name Protein name number 8 weeks 18 weeks 1 AnpepAminopeptidase N P97449 0.4524 0.8517 2 Asah1 Acid ceramidase Q9WV540.6247 0.0186 3 Aspn Asporin Q99MQ4 0.2619 0.0068 4 Atp1b2Sodium/potassium-transporting ATPase subunit beta-2 P14231 0.6055 0.08945 Atrn Attractin Q9WU60 0.7464 0.0079 6 Cacna2d1 Voltage-dependentcalcium channel subunit alpha- O08532 0.6186 0.1576 2/delta-1 7 Cadm1Cell adhesion molecule 1 Q8R5M8 0.8260 0.0670 8 Chl1 Neural celladhesion molecule 1 P70232 0.9771 0.0258 9 Clu Clusterin Q06890 0.70980.1500 10 Cpm Carboxypeptidase M Q80V42 0.3680 0.1460 11 Ctsd CathepsinD P18242 0.5680 0.0176 12 Dpp4 Dipeptidyl peptidase 4 P28843 0.28110.1521 13 Ecm1 Extracellular matrix protein 1 Q61508 0.9629 0.0322 14Fap Seprase P97321 0.6198 0.1000 15 Flt4 Vascular endothelial growthfactor receptor 3 P35917 0.9818 0.1180 16 Fn1 Fibronectin P11276 0.75360.2586 17 Gba Glucosylceramidase P17439 0.2033 0.0070 18 Golph2 Golgiphosphoprotein 2 Q91XA2 0.2742 0.0114 19 Hyou1 Hypoxia up-regulatedprotein 1 Q9JKR6 0.4711 0.0352 20 L1cam Neural cell adhesion molecule L1P11627 0.5814 0.7871 21 Lamp1 Lysosome-associated membrane glycoprotein1 P11438 0.7962 0.0939 22 Lamp2 Lysosome-associated membraneglycoprotein 2 P17047 0.0206 0.0504 23 Lgals3bp Cyclophilin C-associatedprotein O35649 0.4300 0.0800 24 Lifr Leukemia inhibitory factor receptorP42703 0.3391 0.0066 25 Lrp1 Low-density lipoprotein receptor-relatedprotein 1 Q91ZX7 0.6288 0.0336 26 Ncam1 Neural cell adhesion molecule 1P13594 0.7807 0.0412 27 Nptn Neuroplastin P97300 0.3157 0.7977 28 PgcpPlasma glutamate carboxypeptidase Q9WVJ3 0.8894 0.0635 29 PigrPolymeric-immunoglobulin receptor O70570 0.4333 0.3961 30 Plxnb2 PlexinB2 Q3UH76 0.4965 0.0243 31 Pnliprp1 Pancreatic lipase-related protein 1Q5BKQ4 0.9855 0.0194 32 Postn Periostin Q62009 0.2395 0.0954 33 Prom1Prominin-1 O54990 0.8580 0.3555 34 Psap Sulfated glycoprotein 1 Q612070.6845 0.1519 35 Ptprj Receptor-type tyrosine-protein phosphatase etaQ64455 0.7613 0.1003 36 Ptprk Receptor-type tyrosine-protein phosphatasekappa P35822 0.6358 0.0095 37 Sirpa Tyrosine-protein phosphatasenon-receptor type P97797 0.7780 0.1677 substrate 1 38 Thbs1Thrombospondin 1 P35441 NA 0.0110 39 Tnc Tenascin Q80YX1 0.9564 0.192040 Vasn Vasorin Q9CZT5 0.4737 0.1717 41 Vtn Vitronectin P29788 0.34330.2021 Table 2: List of 41 serum glycoproteins measured by SRM in murinesera from controls and mice with prostate cancer at 8 and 18 weeks ofage. p-values below 0.05 indicate a statistical significant differencebetween the normal mice (n = 3) and mice with prostate cancer (n = 3)for the corresponding protein. Experiments were performed on 8 and18-week old mice. Gene name, Protein name (shortened) and Accessionnumber are defined as given in Table 1.

Highly sensitive and selective analyses were performed by monitoringfragmentation channels specific to each peptide of interest in the seraof control mice (healthy) and mice with prostate cancer (cancerous). Thehuman orthologues of the potential biomarkers detected in mouse werethen validated in human sera using standard ELISA techniques and againtargeted mass spectrometry.

(III) Multivariate statistical methods: Rationales and advantages onusing multivariate methods: Signatures or combination of biomarkerdetection can lead to increased diagnostic accuracy, when compared withthe use of single biomarker detection. This is the case when total andfree PSA are used at the same time to diagnose prostate cancer. In ourcase, we have measured a panel of candidate biomarkers and we can nowask what signatures can best discriminate between BPH and localizedprostate cancer (locPCa) or between localized and non-localized, i.e.metastatic prostate cancer (metPCa). Moreover we can find out what arethe biomarkers commonly shared in all signatures, making them highlyvaluable in terms of intellectual property. In order to classifypatients based on a biomarker signature, we performed quadraticdiscriminance analysis. The goal of the discriminance analysis is todetermine a rule by which an individual is allocated to one of 2 or moregroups (e.g. BPH and locPCa), based on the independent variables(biomarkers) that are measured in such an individual. The parametersthat describe this rule are computed from the analysis of variables ofall individuals with already known classification. In order to estimatethe bias of the discriminant rule, we apply Jacknife leave one-out crossvalidation. Analyses were performed using the statistical softwarepackages SYSTAT 12 and SPSS14.0.

Results:

Initially, we extracted N-glycopetides from the perfused prostate tissueand serum of both control and cancer-bearing mice. We identified intotal 642 glycoproteins from prostate tissue and 253 glycoproteins fromserum. 110 proteins were commonly detected. We could thus generate acatalog comprising of 785 N-glycoproteins in total. From the initialmouse glycoprotein catalog, we could quantify 279 glycoproteins fromtissue and 160 glycoproteins from serum comparing samples from mice withcancer and their respective controls (FIG. 2). Out of these proteins,165 glycoproteins fulfilling at least one of the rationales listed inTable 1 were found to be potential biomarkers and therefore chosen forfurther verification.

Using SRM on the murine serum samples, we could verify and quantify 41out of the 165 initial candidates. (Table 2)

46 candidate biomarkers which were either already tested in mice seravia SRM or promising candidates that showed up in the initial discoveryphase from murine prostate tissue (Table 3) were further validated on 52human serum samples. This was done by applying ELISA and SRM.

TABLE 3 List of 43 serum glycoproteins measured in human sera TechniqueGene Accession used name Protein name number for analysis 1 AGTR1 Type-1angiotensin II receptor P30556 SRM 2 AKAP13 A-kinase anchor protein 13Q12802 SRM 3 AOC3 Membrane copper amine oxidase Q16853 SRM 4 APOBApolipoprotein B-100 P04114 SRM 5 ASPN Asporin Q9BXN1 SRM 6 ATRNAttractin O75882 SRM 7 AZGP1 Zinc-alpha-2-glycoprotein P25311 SRM 8CADM1 Cell adhesion molecule 1 Q9BY67 SRM 9 CEACAM1 Carcinoembryonicantigen-related cell P13688 ELISA, SRM adhesion molecule 1 10 CFHComplement factor H P08603 SRM 11 CLU Clusterin precursor P10909 SRM 12CP Ceruloplasmin P00450 SRM 13 CPM Carboxypeptidase M P14384 SRM 14 CTSDCathepsin D P07339 SRM 15 ECM1 Extracellular matrix protein 1 Q16610ELISA, SRM 16 EFNA5 Ephrin-A5 P52803 SRM 17 F5 Coagulation factor VP12259 SRM 18 FAM3D Protein FAM3D Q96BQ1 ELISA 19 GALNTL4 Putativepolypeptide N-acetylgalactosaminyl- Q6P9A2 SRM transferase-like protein4 20 GOLPH2 Golgi phosphoprotein 2 Q8NBJ4 SRM 21 GRN Granulins P28799ELISA 22 GSPT1 Eucariotic peptide chain release factor P15170 SRMGTP-binding subunit ERF3A 23 HYOU1 Hypoxia up-regulated protein 1 Q9Y4L1SRM 24 KIT Mast/stem cell growth factor receptor P10721 SRM 25 KLK3Prostate-specific antigen P07288 ELISA, SRM 26 L1CAM Neural celladhesion molecule L1 P32004 SRM 27 LGALS3BP Galectin-3-binding proteinQ08380 ELISA, SRM 28 LOX Protein-lysine 6-oxidase P28300 SRM 29 LRP1Prolow-density lipoprotein receptor- Q07954 SRM related protein 1 30 MMENeprilysin P08473 ELISA 31 MMP1 Interstitial collagenase P03956 SRM 32NCAM1 Neural cell adhesion molecule 1 P13591 SRM 33 OLFM4 Olfactomedin-4Q6UX06 SRM 34 PGCP Plasma glutamate carboxypeptidase Q9Y646 SRM 35 PIGRPolymeric immunoglobulin receptor P01833 ELISA 36 POSTN Periostin Q15063ELISA 37 PSAP Proactivator polypeptide P07602 SRM 38 SEMA4DSemaphorin-4D Q92854 SRM 39 TFRC Transferrin receptor protein 1 P02786SRM 40 THBS1 Thrombospondin-1 P07996 ELISA, SRM 41 TIMP1Metalloproteinase inhibitor 1 P01033 ELISA, SRM 42 TM9SF3 TM9SF3 proteinQ8WUB5 SRM 43 VTN Vitronectin P04004 SRM 44 ICAM1 Intercellular adhesionmolecule 1 P05362 SRM 45 CPE Carboxypeptidase E P16870 ELISA 46 MSMBBeta-microseminoprotein P08118 ELISA Table 3: List of 46 serumglycoproteins measured in human sera. The selected biomarker candidateswere either analyzed by SRM or ELISA. Gene name, Protein name(shortened) and Accession number are defined as given in Table 1.

Statistical Analysis

Following statistical analysis, we could identify a 3-biomarkersignature comprising of Asporin (ASPN), Vitronectin (VTN) and Membranecopper amine oxidase (AOC3). The Signature had an accuracy of 81% indiscriminating between BPH (n=15) and locPCa (n=16) patients; this meansthat 81% of the patients analyzed were correctly diagnosed by our3-biomarker signature. AOC3 was found to be the weakest contributor.Thus we substituted this protein with other potential biomarkers andkept the ones gaining similar or higher accuracy (≧80%). The followingproteins could be individually added in this way: LOX, PGCP, PSAP, THBS1(FIG. 3 A).

The discrimination of PSA itself was measured as well which resulted inan accuracy of 71% discriminating between BPH (n=15) and locPCa (n=16)patients.

Additionally, we added PSA data to the core signature of ASPN and VTN.By including one of the following proteins: AOC3, CFH, CLU, KIT, LOX,TFRC, THBS1, LGALS3BP, GOLPH2, accuracies of up to 90% was achieved(FIG. 3 B).

Following statistical analysis using more data, we could furtheridentify a 5-biomarker signature comprising of Asporin (ASPN), CathepsinD (CTSD), Hypoxia up-regulated protein 1 (HYOU1) and Olfactomedin-4(OLFM4). The Signature had an accuracy of 87% in discriminating betweenBPH (n=35) and locPCa (n=41) patients; this means that 87% of thepatients analyzed were correctly diagnosed by our 5-biomarker signature.The discrimination of PSA itself was measured as well which resulted inan accuracy of 72% discriminating between BPH (n=41) and locPCa (n=64)patients (FIG. 3C).

Additionally, by removing in each case only one of these four proteinsan accuracy of up to 83% was achieved (FIG. 3C).

Using the same dataset and applying a somewhat less stringent criterionfor selection out of the systems according to table 3, a refined list ofbiomarkers was determined and is collected in table 4. An assay with agroup of at least three of the systems given in table 4 in combinationwith a PSA (ELISA) measurement leads to an accuracy of around 80% oreven higher. A selection of at least four of the systems given in table4 in combination with a PSA (ELISA) measurement even leads to anaccuracy of around 85% or higher.

The threshold values for each of the systems given in table 4 indicatesthe concentration threshold above or below (as indicated) which apositive diagnosis can be issued. If all of the markers in one assay(for example in a group of 3 biomarkers selected from table 4) exceed inconcentration above these concentration values a positive diagnosis canbe issued with the accuracies as given above.

TABLE 4 List of 15 serum glycoproteins measured in human sera TechniqueAccession used basic preferred Gene name Protein name number foranalysis conc conc 1 AKAP13 A-kinase anchor protein 13 Q12802SRM >2500 >2800 2 ASPN Asporin Q9BXN1 SRM >55 >60 3 CFH Complementfactor H P08603 SRM <250000 <231500 4 CP Ceruloplasmin P00450 SRM<120000 <101500 5 CPE Carboxypeptidase E P16870 ELISA >0.05 >0.075 (OD)(OD) 6 CPM Carboxypeptidase M P14384 SRM <110 <95 7 CTSD Cathepsin DP07339 SRM <32 <25 8 HYOU1 Hypoxia up-regulated protein 1 Q9Y4L1SRM >35 >40 9 ICAM1 Intercellular adhesion molecule P05362 SRM <360 <3401 10 LGALS3BP Galectin-3-binding protein Q08380 SRM <400 <390 11 MSMBBeta-microseminoprotein P08118 ELISA >0.12 >0.15 (OD) (OD) 12 OLFM4Olfactomedin-4 Q6UX06 SRM <20 <15 13 TM9SF3 TM9SF3 protein Q8WUB5SRM >8 >10 14 VTN Vitronectin P04004 SRM <3500 <3300 15 GALNTL4 Putativepolypeptide N- Q6P9A2 SRM <15 <10 acetylgalactosaminyltransferase- likeprotein 4 Table 4: Refined list of 15 serum glycoproteins measured inhuman sera after statistical analysis (BPH (n = 35) and locPCa (n =41)). Gene name, Protein name (shortened) and Accession number aredefined as given in Table 1. In a first column the basic concentrationthreshold values in ng/ml are given, and in a second column thepreferred concentration threshold in ng/ml values are given. Where OD isindicated measurement takes place at 405 nm and relative values aregiven using commercially available antibodies (CPE: R&Dsystems,polyclonal: Nr. AF3587 and R&Dsystems, monoclonal: MAB3587; MSMB:R&Dsystems, polyclonal: Nr. AF3780 and Abnova, monoclonal:H00004477-M08).b) Using a biomarker signature comprising of the following biomarkers:Asporin (ASPN), Vitronectin (VTN), Cathepsin D (C STD), PolypeptideN-acetyl-galactosaminyltransferase GALNTL4, Proactivator polypeptide(PSAP), and Thrombospondin-1 (THBS-1), we could correctly distinguishbetween locPCa (n=16) and metPCa (n=21) patients in 100% of the cases.PSAP was found to be the weakest contributor. Leaving it out, still 97%accuracy in the discriminant analysis was achieved. Thus we substitutedthis protein with other potential biomarkers and kept the onesameliorating the accuracy (>97%). The following protein could beindividually added in this way: CEACAM1, EFNA5, GSPT1, HYOU1, KIT (allgaining an accuracy of 100%) (FIG. 3).

It should be noted that any of the systems as given in table 3,preferably at the combination of two, most preferably as a combinationof at least three (or exactly 3), of at least four (or exactly 4) or ofat least five (or exactly 5) glycoproteins can be an assay which shallbe covered by the present invention. The specific statisticallyevaluated systems as outlined above are just those which for thediagnostic aspects addressed in these statistical tests could be shownto be most powerful. For different diagnostic/prognostic/therapeuticaspects or using different statistical evaluation methods, differentcombinations might also be possible and shall be regarded as accordingto the present invention.

1-16. (canceled)
 17. A method for the determination of a cancer diagnostic or prognostic biomarker assay including the following steps: (a) identification of potential candidate protein or peptide biomarkers and drug-target based on the measurement of protein or peptide constituent concentrations in tissue sample proteomes as well as sample proteomes of serum, plasma or a derivative of blood, or blood itself derived from healthy non-human mammalian individuals as well as from cancerous non-human mammalian individuals and qualitatively selecting as potential candidate protein/peptide biomarkers those which show a pronounced differential behaviour between healthy and cancerous sample proteomes; (b) optional verification of the potential candidate protein/peptide biomarkers as identified in step (a) by quantitative mass spectrometric measurement of the potential candidate protein biomarkers in sample proteomes of serum, plasma or a derivative of blood, or blood itself derived from healthy non-human mammalian individuals as well as from cancerous non-human mammalian individuals and selecting as candidate protein or peptide biomarkers those which show a mass-spectrometrically measurable quantitative differential behaviour between healthy and cancerous sample proteomes; (c) validation of the candidate protein or peptide biomarkers as identified in step (a), or as optionally verified in step (b), by mass spectrometric measurement or affinity reagent-based determination of the candidate protein biomarkers in sample proteomes of serum, plasma or a derivative of blood, or blood itself derived from healthy human individuals as well as from cancerous human individuals and selecting as protein or peptide biomarkers those which show a mass-spectrometrically measurable or affinity reagent-based assay detectable differential behaviour between healthy and cancerous sample proteomes; (d) application of statistical methods to uncover single or groups of protein or peptide biomarkers as validated in step (c) as signatures for the detection of patients with cancer or for the prognosis of cancer and/or the stratification of patients.
 18. The method according to claim 17, wherein the cancerous sample proteomes are sample proteomes of individuals with prostate cancer, and wherein the tissue samples are prostate tissue samples, and wherein the protein/peptide biomarkers are selected to be diagnostic of prostate cancer.
 19. The method according to claim 17, wherein the selection criteria for step (a) are selected from the group of: potential biomarkers regulated in prostate tissue and serum; potential biomarkers regulated in prostate tissue and detected in serum; potential biomarkers regulated in prostate tissue and secreted; potential biomarkers exclusively detected in prostate tissue and sera of mice with cancer; potential biomarkers, specific for prostate and regulated in cancer tissue or serum; potential biomarkers specific for prostate and secreted; potential biomarkers top regulated in prostate tissue or serum, by a factor of more than four; potential biomarkers, prior knowledge-based selection, characterised by known biological function during cancer progression; or a combination thereof.
 20. The method according to claim 17, wherein in step (a) the proteins or peptides of the digested proteins of the samples are in a first step identified by using a shotgun mass spectrometric technique, and in a second step a combined liquid chromatography/mass spectrometry technique, is used for the identification of the differential properties between healthy and cancerous samples.
 21. The method according to claim 17, wherein the affinity reagent-based method or assay is an antibody-based method or assay within step (c) and is selected to be an Enzyme-Linked Immunosorbent Assay (ELISA) or a Multiplex Bead Array Assay.
 22. A cancer diagnostic or therapeutic or prognostic or patient stratification biomarker assay determinable or determined using a method according to claim
 17. 23. A cancer diagnostic or therapeutic or prognostic or patient stratification biomarker assay for the diagnosis or therapy of prostate cancer comprising the measurement of at least two protein or peptide biomarkers determined according to a method according to claim 17 in human serum, plasma or a derivative of blood, or blood itself.
 24. A method for the diagnosis or for the therapy or for the monitoring of localized prostate cancer, for the distinction from benign prostate hyperplasia, using a combined measurement of the concentration of at least two, or at least three proteins or fragments of proteins selected from the group derived from: ASPN; VTN; AOC3; LOX; PGCP; PSAP; THBS1; CFH; CLU; KIT; TFRC; LGALS3BP; GOLPH2; HYOU1; CTSD; OLFM4; AKAP13; CP; CPE; CPM; ICAM1; MSMB; TM9SF3; GALNTL4 in human serum, plasma or a derivative of blood, or blood itself.
 25. The method for the diagnosis or for the therapy or for the monitoring of localized prostate cancer according to claim 24, using a combined measurement of the concentration of ASPN derived protein or fragments thereof, in combination with VTN derived protein or fragments thereof, in human serum, plasma or a derivative of blood, or blood itself.
 26. A method for the diagnosis or for the therapy or for the monitoring of localized prostate cancer for the distinction from benign prostate hyperplasia, using a combined measurement of the concentration of at least three proteins or fragments of proteins selected from the group derived from: ASPN; HYOU1; CTSD; OLFM4; in human serum, plasma or a derivative of blood, or blood itself, and wherein for the diagnosis/monitoring additionally the concentration of the Prostate Specific Antigen (PSA) in the human serum, plasma or a derivative of blood, or blood itself is measured using an affinity reagent-based assay.
 27. The method for the diagnosis or for the therapy or for the monitoring of localized prostate cancer according to claim 26, wherein for a positive diagnosis or the monitoring of localized prostate cancer at least one or a combination of the following concentration criteria has to be met: the concentration of ASPN derived protein or fragments thereof has to be more than 55 ng/ml, the concentration of HYOU1 derived protein or fragments thereof has to be more than 35 ng/ml, the concentration of CTSD derived protein or fragments thereof has to be less than 32 ng/ml, the concentration of OLFM4 derived protein or fragments thereof has to be less than 20 ng/ml.
 28. A method for the diagnosis or for the therapy or for the monitoring of non-localized prostate cancer, for the distinction from localized prostate cancer, using a combined measurement of the concentration of ASPN and CTSD and THBS1 and GALNTL4 as well as VTN derived protein or fragments thereof in human serum, plasma or a derivative of blood, or blood itself.
 29. The method for the diagnosis or for the therapy or for the monitoring of non-localized prostate cancer according to claim 28, for the distinction from localized prostate cancer using a combined measurement of the concentration of ASPN and CTSD and THBS1 and GALNTL4 as well as VTN derived protein or fragments thereof in human serum, plasma or a derivative of blood, or blood itself, in combination with the measurement of one further protein or fragments thereof selected from the group derived from: PSAP; GSPT1; CEACAM1; HYOU1; EFNA5; KIT.
 30. The method for the diagnosis and/or for the therapy or for the monitoring of non-localized prostate cancer according to claim 28, wherein for a positive diagnosis of metastatic prostate cancer the combination of the following concentration criteria has to be met: the concentration of ASPN derived protein or fragments thereof has to be more than 60 ng/ml and at the same time; the concentration of CTSD derived protein or fragments has to be more than 120 ng/ml and at the same time; the concentration of THBS1 derived protein or fragments has to be less than 12000 ng/ml and at the same time; the concentration of GALNTL4 derived protein or fragments has to be more than 1400 ng/ml and at the same time; and the concentration of VTN derived protein or fragments has to be more than 3000 ng/ml.
 31. The method according to claim 24, wherein for the diagnosis or monitoring additionally the concentration of the Prostate Specific Antigen (PSA) in the human serum, plasma or a derivative of blood, or blood itself is measured using an affinity reagent-based, namely an antibody-based Enzyme-Linked Immunosorbent Assay (ELISA), and wherein for a positive diagnosis the concentration of the Prostate Specific Antigen has to be more than 2 ng/ml, and wherein specifically for the diagnosis or monitoring of localized prostate cancer the concentration of the Prostate Specific Antigen has to be more than 2 ng/ml, and wherein for a positive diagnosis of non-localized prostate cancer the concentration of the prostate specific antigen has to be more than 100 ng/ml.
 32. The method according to claim 24 for staging or diagnostic purposes, for the early detection and diagnosis of localized prostate cancer, or for therapy selection or for therapy monitoring.
 33. The method according to claim 17, wherein the cancerous sample proteomes are sample proteomes of individuals with prostate cancer, wherein the tissue samples are prostate tissue samples, wherein samples with localized or non-localized prostate cancer are used, and wherein the protein/peptide biomarkers are selected to be diagnostic of prostate cancer.
 34. The method according to claim 17, wherein in step (a) proteins derived from the sample proteomes are selected to be exclusively N-linked glycoproteins, and wherein in a first step the proteome of the sample is digested, and subsequently extracted using solid-phase extraction, and wherein the biomarkers are N-linked glycoproteins or peptide fragments thereof.
 35. The method according to claim 17, wherein the non-human mammalian individuals are mice, and wherein the mice prostate tissue for the samples in step (a) is perfused for complete removal of blood from the prostate tissue prior to the analysis or further treatment of the proteome.
 36. The method according to claim 19, wherein selection takes place if the factor between signals of healthy compared with signals of cancerous samples is larger than 1.5 or smaller than 0.75.
 37. The method according to claim 19, wherein selection takes place if the factor between signals of healthy compared with signals of cancerous samples is than 1.75 and smaller than 0.5.
 38. The method according to claim 17, wherein in step (b) absolute quantification is achieved by using a quantitative internal specifically synthesised internal standard.
 39. The method according to claim 17, wherein in step (b) or in step (c) selected reaction monitoring (SRM), optionally in combination with liquid chromatography, is used as mass spectrometry method.
 40. The method according to claim 17, wherein within step (a) the mass spectrometrically detected proteins or protein fragment signals are attributed to the corresponding proteins by using database information.
 41. A cancer diagnostic or therapeutic or prognostic or patient stratification biomarker assay for the diagnosis or therapy of prostate cancer comprising the measurement of at least three protein peptide biomarkers determined according to a method according claim 17 in human serum, plasma or a derivative of blood, or blood itself, in combination with an affinity reagent-based antibody-based assay for the detection of Prostate Specific Antigen (PSA).
 42. The method for the diagnosis or for the therapy or for the monitoring of localized prostate cancer, for the distinction from benign prostate hyperplasia, according to claim 24 using a combined measurement of the concentration of or at least three proteins or fragments of proteins selected from the group derived from: ASPN; VTN; AOC3; LOX; PGCP; PSAP; THBS1; CFH; CLU; KIT; TFRC; LGALS3BP; GOLPH2; HYOU1; CTSD; OLFM4; AKAP13; CP; CPE; CPM; ICAM1; MSMB; TM9SF3; GALNTL4 in human serum, plasma or a derivative of blood, or blood itself.
 43. The method for the diagnosis or for the therapy or for the monitoring of localized prostate cancer, for the distinction from benign prostate hyperplasia, according to claim 24 the measurement is carried out using tandem mass spectrometry techniques, namely selected reaction monitoring (SRM), optionally in combination with liquid chromatography, aor Enzyme-Linked Immunosorbent Assays (ELISA) for the detection of these proteins or fragments thereof.
 44. The method for the diagnosis or for the therapy or for the monitoring of localized prostate cancer according to claim 24, using a combined measurement of the concentration of ASPN derived protein or fragments thereof, optionally in combination with VTN derived protein or fragments thereof, in human serum, plasma or a derivative of blood, or blood itself, in combination with the measurement of one further protein or fragments thereof selected from the group derived from: AOC3; LOX; PGCP; PSAP; THBS1; CFH; CLU; KIT; TFRC; LGALS3BP; GOLPH2; HYOU1; CTSD; OLFM4.
 45. The method ethod for the diagnosis or for the therapy or for the monitoring of localized prostate cancer according to claim 25, wherein for a positive diagnosis or the monitoring of localized prostate cancer the concentration of ASPN derived protein or fragments thereof has to be more than 55 ng/ml, and at the same time, at least one of the following concentration criteria has to be met: the concentration of VTN derived protein or fragments has to be less than 3500 ng/ml, the concentration of AOC3 derived protein or fragments thereof has to be less than 250 ng/ml, the concentration of LOX derived protein or fragments thereof has to be less than 580 ng/ml, the concentration of PGCP derived protein or fragments thereof has to be more than 550 ng/ml, the concentration of PSAP derived protein or fragments thereof has to be less than 33000 ng/ml, the concentration of THBS1 derived protein or fragments thereof has to be more than 12500 ng/ml, the concentration of LGALS3BP derived protein or fragments thereof has to be more than 390 ng/ml, the concentration of GOLPH2 derived protein or fragments thereof has to be more than 80 ng/ml, the concentration of HYOU1 derived protein or fragments thereof has to be more than 35 ng/ml, the concentration of CTSD derived protein or fragments thereof has to be less than 32 ng/ml, the concentration of OLFM4 derived protein or fragments thereof has to be less than 20 ng/ml.
 46. A method for the diagnosis or for the therapy or for the monitoring of localized prostate cancer for the distinction from benign prostate hyperplasia, using a combined measurement of the concentration of at least three proteins or fragments of proteins selected from the group derived from: ASPN; HYOU1; CTSD; OLFM4; in human serum, plasma or a derivative of blood, or blood itself, and wherein for the diagnosis or monitoring additionally the concentration of the Prostate Specific Antigen (PSA) in the human serum, plasma or a derivative of blood, or blood itself is measured using an affinity reagent-based, an antibody-based assay, and wherein for a positive diagnosis the concentration of the Prostate Specific Antigen has to be more than 4 ng/ml.
 47. The method for the diagnosis or for the therapy or for the monitoring of localized prostate cancer according to claim 26, wherein for a positive diagnosis or the monitoring of localized prostate cancer at least one or a combination of the following concentration criteria has to be met: the concentration of ASPN derived protein or fragments thereof has to be more than 60 ng/ml, and/or the concentration of HYOU1 derived protein or fragments thereof has to be more than 40 ng/ml, and/or the concentration of CTSD derived protein or fragments thereof has to be less than 25 ng/ml, and/or the concentration of OLFM4 derived protein or fragments thereof has to be less than 15 ng/ml.
 48. The method for the diagnosis or for the therapy or for the monitoring of non-localized prostate cancer, according to claim 28, wherein the measurement is carried out using tandem mass spectrometry techniques, namely selected reaction monitoring (SRM), optionally in combination with liquid chromatography, or Enzyme-Linked Immunosorbent Assays (ELISA) for the detection of these proteins or fragments thereof
 49. The method for the diagnosis or for the therapy or for the monitoring of non-localized prostate cancer according to claim 28, wherein for a positive diagnosis of metastatic prostate cancer at least one or a combination of the following concentration criteria has to be met: the concentration of ASPN derived protein or fragments thereof has to be more than 68 ng/ml and at the same time, the concentration of CTSD derived protein or fragments has to be more than 133 ng/ml and at the same time, the concentration of THBS1 derived protein or fragments has to be less than 10750 ng/ml and at the same time, the concentration of GALNTL4 derived protein or fragments has to be more than 1650 ng/ml and at the same time, the concentration of VTN derived protein or fragments has to be more than 3300 ng/ml, wherein if, additionally measured, the concentration of PSAP derived protein or fragments thereof has to be more than 34000 ng/ml, or the concentration of GSPT1 derived protein or fragments thereof has to be more than 510 ng/ml, or the concentration of CEACAM1 derived protein or fragments thereof has to be more than 38 ng/ml, or the concentration of HYOU1 derived protein or fragments thereof has to be more than 89 ng/ml, or the concentration of EFNA5 derived protein or fragments thereof has to be more than 65 ng/ml, or the concentration of KIT derived protein or fragments thereof has to be more than 95 ng/ml.
 50. The method according to claim 24, wherein for the diagnosis or monitoring additionally the concentration of the Prostate Specific Antigen (PSA) in the human serum, plasma or a derivative of blood, or blood itself is measured using an affinity reagent-based, namely an antibody-based Enzyme-Linked Immunosorbent Assay (ELISA), and wherein for a positive diagnosis the concentration of the Prostate Specific Antigen has to be more than 4 ng/ml, and wherein specifically for the diagnosis/monitoring of localized prostate cancer the concentration of the Prostate Specific Antigen has to be more than more than 8 ng/ml, and wherein for a positive diagnosis of non-localized prostate cancer the concentration of the prostate specific antigen has to be more than 175 ng/ml.
 51. The method for the diagnosis and/or for the therapy or for the monitoring of non-localized prostate cancer according to claim 29, wherein for a positive diagnosis of metastatic prostate cancer the combination of the following concentration criteria has to be met: the concentration of ASPN derived protein or fragments thereof has to be more than 60 ng/ml and at the same time, the concentration of CTSD derived protein or fragments has to be more than 120 ng/ml and at the same time, the concentration of THBS1 derived protein or fragments has to be less than 12000 ng/ml and at the same time, the concentration of GALNTL4 derived protein or fragments has to be more than 1400 ng/ml and at the same time, the concentration of VTN derived protein or fragments has to be more than 3000 ng/ml. 